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Article: On a mixture GARCH time-series model

TitleOn a mixture GARCH time-series model
Authors
KeywordsGARCH
MGARCH
Stochastic difference equation
Tail behaviour
Volatility clustering
Issue Date2006
PublisherBlackwell Publishing Ltd.
Citation
Journal Of Time Series Analysis, 2006, v. 27 n. 4, p. 577-597 How to Cite?
AbstractRecently, there has been a lot of interest in modelling real data with a heavy-tailed distribution. A popular candidate is the so-called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of GARCH models are not thick enough in some applications. In this paper, we propose a mixture generalized autoregressive conditional heteroscedastic (MGARCH) model. The stationarity conditions and the tail behaviour of the MGARCH model are studied. It is shown that MGARCH models have tails thicker than those of the associated GARCH models. Therefore, the MGARCH models are more capable of capturing the heavy-tailed features in real data. Some real examples illustrate the results. © 2006 Blackwell Publishing Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/82777
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 1.177
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zen_HK
dc.contributor.authorLi, WKen_HK
dc.contributor.authorYuen, KCen_HK
dc.date.accessioned2010-09-06T08:33:18Z-
dc.date.available2010-09-06T08:33:18Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of Time Series Analysis, 2006, v. 27 n. 4, p. 577-597en_HK
dc.identifier.issn0143-9782en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82777-
dc.description.abstractRecently, there has been a lot of interest in modelling real data with a heavy-tailed distribution. A popular candidate is the so-called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of GARCH models are not thick enough in some applications. In this paper, we propose a mixture generalized autoregressive conditional heteroscedastic (MGARCH) model. The stationarity conditions and the tail behaviour of the MGARCH model are studied. It is shown that MGARCH models have tails thicker than those of the associated GARCH models. Therefore, the MGARCH models are more capable of capturing the heavy-tailed features in real data. Some real examples illustrate the results. © 2006 Blackwell Publishing Ltd.en_HK
dc.languageengen_HK
dc.publisherBlackwell Publishing Ltd.en_HK
dc.relation.ispartofJournal of Time Series Analysisen_HK
dc.rightsJournal of Time Series Analysis. Copyright © Blackwell Publishing Ltd.en_HK
dc.subjectGARCHen_HK
dc.subjectMGARCHen_HK
dc.subjectStochastic difference equationen_HK
dc.subjectTail behaviouren_HK
dc.subjectVolatility clusteringen_HK
dc.titleOn a mixture GARCH time-series modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0143-9782&volume=27&issue=4&spage=577&epage=597&date=2006&atitle=On+a+mixture+GARCH+time+series+modelen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.emailYuen, KC: kcyuen@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.identifier.authorityYuen, KC=rp00836en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-9892.2006.00467.xen_HK
dc.identifier.scopuseid_2-s2.0-33745328190en_HK
dc.identifier.hkuros115934en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745328190&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume27en_HK
dc.identifier.issue4en_HK
dc.identifier.spage577en_HK
dc.identifier.epage597en_HK
dc.identifier.isiWOS:000237972600004-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZhang, Z=37060044300en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.scopusauthoridYuen, KC=7202333703en_HK
dc.identifier.citeulike681332-

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